Event-Triggered-Based Distributed Cooperative Energy Management for Multienergy Systems

TLDR

Energy bodies cooperate to maximize day‑ahead social welfare and smooth real‑time load and renewable fluctuations across electricity and heat timescales. The study investigates day‑ahead and real‑time cooperative energy management for multienergy systems and proposes an event‑triggered distributed algorithm to address these issues. The authors model each energy body as a dual supplier‑customer, formulate day‑ahead and real‑time management as a distributed coupled optimization problem, and solve it with an event‑triggered distributed algorithm that runs asynchronously and locally, validated by simulations. The algorithm effectively solves the coupled optimization, enabling each energy body to compute optimal operations locally, improving reliability, scalability, and privacy while dramatically reducing communication through event‑triggered messaging.

Abstract

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.